Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models
PC Chang, JJ Lin, JC Hsieh, J Weng - Applied Soft Computing, 2012 - Elsevier
This study presented a new diagnosis system for myocardial infarction classification by
converting multi-lead ECG data into a density model for increasing accuracy and flexibility of …
converting multi-lead ECG data into a density model for increasing accuracy and flexibility of …
Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals
Myocardial infarction (MI) is detected using electrocardiography (ECG) signals. Machine
learning (ML) models have been used for automated MI detection on ECG signals. Deep …
learning (ML) models have been used for automated MI detection on ECG signals. Deep …
A two-stage mechanism for registration and classification of ECG using Gaussian mixture model
An automatic classifier for electrocardiogram (ECG) based cardiac abnormality detection
using Gaussian mixture model (GMM) is presented here. In first stage, pre-processing that …
using Gaussian mixture model (GMM) is presented here. In first stage, pre-processing that …
Classification of myocardial infarction using multi resolution wavelet analysis of ECG
RS Remya, KP Indiradevi, KKA Babu - Procedia Technology, 2016 - Elsevier
In this paper, classification of anterior and inferior myocardial infarction from normal cases is
done using the changes happening in ECG waves. Depth of Q peak and elevation in ST …
done using the changes happening in ECG waves. Depth of Q peak and elevation in ST …
Heart disease classification based on ECG using machine learning models
SM Malakouti - Biomedical Signal Processing and Control, 2023 - Elsevier
One of the most critical steps when diagnosing cardiovascular disorders is examining and
processing ECG data. Classification of health and ill persons is the primary focus of research …
processing ECG data. Classification of health and ill persons is the primary focus of research …
Artificial Neural Network‐Based Automated ECG Signal Classifier
SH El-Khafif, MA El-Brawany - International Scholarly Research …, 2013 - Wiley Online Library
The ECG signal is well known for its nonlinear dynamic behavior and a key characteristic
that is utilized in this research; the nonlinear component of its dynamics changes more …
that is utilized in this research; the nonlinear component of its dynamics changes more …
A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank
Myocardial infarction (MI), also referred to as heart attack, occurs when there is an
interruption of blood flow to parts of the heart, due to the acute rupture of atherosclerotic …
interruption of blood flow to parts of the heart, due to the acute rupture of atherosclerotic …
Accurate detection of myocardial infarction using non linear features with ECG signals
Interrupted blood flow to regions of the heart causes damage to heart muscles, resulting in
myocardial infarction (MI). MI is a major source of death worldwide. Accurate and timely …
myocardial infarction (MI). MI is a major source of death worldwide. Accurate and timely …
Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework
Electrocardiogram is the P-QRS-T wave representing the cardiac depolarization and re-
polarization, recorded at the body surface. The subtle changes in amplitude and duration of …
polarization, recorded at the body surface. The subtle changes in amplitude and duration of …
Developing of robust and high accurate ECG beat classification by combining Gaussian mixtures and wavelets features
AM Alqudah, A Albadarneh, I Abu-Qasmieh… - Australasian physical & …, 2019 - Springer
Electrocardiogram (ECG) beat classification is a significant application in computer-aided
analysis and diagnosis technologies. This paper proposed a method to detect, extract …
analysis and diagnosis technologies. This paper proposed a method to detect, extract …